JustinaPetr

pipeline:
- name: "intent_featurizer_count_vectors"
- name: "intent_classifier_tensorflow_embedding"
  intent_tokenization_flag: true
  intent_split_symbol: "+"

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interpreter = Interpreter.load('models/current/nlu_model')
print(interpreter.parse("Yes. Can you give me suggestions on how to get there?"))

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{  
   'intent':{  
      'name':'affirm+ask_transport',
      'confidence':0.918471097946167
   },
   'entities':[  

   ],
   'intent_ranking':[  
      {  

JustinaPetr

templates:
  utter_greet:
    - "Hey, how can I help you?"
  utter_goodbye:
    - "Talk to you later!"
    - "Goodbye :("
    - "Bye!"
  utter_affirm:
    - "Cool. Let me book the spot for you."
    - "Glad to hear. I'll make an RSVP for you."

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{
  "actions": [
    {
      "description": "Default Welcome Intent",
      "name": "MAIN",
      "fulfillment": {
        "conversationName": "<INSERT YOUR CONVERSATION NAME HERE>"
      },
      "intent": {
        "name": "actions.intent.MAIN",

JustinaPetr

{
   "actions": [
      {
        "description": "Default Welcome Intent",
        "name": "MAIN",
        "fulfillment": {
          "conversationName": "welcome"
        },
        "intent": {
          "name": "actions.intent.MAIN",

JustinaPetr

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import logging

from flask import Flask, Response, Blueprint, request, jsonify
from rasa_core.channels.channel import UserMessage, OutputChannel
from rasa_core.channels.channel import InputChannel

JustinaPetr

class GoogleAssistant(InputChannel):

  @classmethod
  def name(cls):

  def blueprint(self, on_new_message):

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@classmethod
def name(cls)
  return ‘google_assistant’

JustinaPetr

def blueprint(self, on_new_message):
	google_webhook = Blueprint('google_webhook', __name__)